In this section, we show how the convolution may be implemented more efficiently using steerable functions[1]}, [2]}.
A function is said to be steerable if it can be expressed as a linear combination of equivariant functions[1]}.
By structuring a PCN such that the learnable filters \(\Phi \)  are expressed in an appropriate equivariant basis, it is possible to efficiently sample convolutions at arbitrary points by evaluating \(\Phi \)  only once.
